Dependence Modeling with Copulas

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A01=Harry Joe
advanced probability theory
Archimedean Copula
Author_Harry Joe
BIC Value
Bivariate Copula
Bivariate Copula Families
Bivariate Margins
Category=KCH
Category=PBT
Conditional Cdf
Copula Density
Copula Families
Copula Models
dependence and tail properties of multivariate distributions
dependence modeling techniques
dependence structures and tail properties of copulas
divergences
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Factor Copula
families
GARCH Parameter
gaussian
generalizations of vine copula models
Hierarchical Archimedean Copula
high-dimensional copula applications
high-dimensional data inference
kullback
leibler
Log ?L
Log λL
Lower Tail Dependence
margins
models
multivariate dependence structure modeling
Multivariate Extreme
multivariate statistical analysis
Pair Copula Construction
parametric
parametric copula families
Positive Dependence Condition
Regular Vine
stochastic simulation algorithms
tail
Tail Asymmetry
Tail Dependence
Tail Dependence Parameters
Tail Order
tail risk assessment
Tree T2
univariate
Vine Copula
vine copula modeling of high-dimensional data

Product details

  • ISBN 9781032477374
  • Weight: 453g
  • Dimensions: 178 x 254mm
  • Publication Date: 21 Jan 2023
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Dependence Modeling with Copulas covers the substantial advances that have taken place in the field during the last 15 years, including vine copula modeling of high-dimensional data. Vine copula models are constructed from a sequence of bivariate copulas. The book develops generalizations of vine copula models, including common and structured factor models that extend from the Gaussian assumption to copulas. It also discusses other multivariate constructions and parametric copula families that have different tail properties and presents extensive material on dependence and tail properties to assist in copula model selection.

The author shows how numerical methods and algorithms for inference and simulation are important in high-dimensional copula applications. He presents the algorithms as pseudocode, illustrating their implementation for high-dimensional copula models. He also incorporates results to determine dependence and tail properties of multivariate distributions for future constructions of copula models.

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